Transcriptome and targeted metabolome analysis revealed the effects of combined red and blue light on the growth and secondary metabolism of Scutellaria baicalensis Georgi

Abstract

Scutellaria baicalensis Georgi is a medicinal plant commonly used in China and other parts of East Asia. A high flavonoid content endows it with multiple beneficial biological activities that have anti-inflammatory, antibacterial, antiviral, and anti-COVID19 properties. Light-emitting diodes (LEDs) have been recognized as effective artificial lights to enhance plant growth and the accumulation of secondary metabolites for commercial plant production. However, little is known regarding the effects of LEDs on S. baicalensis. Here, we explored the effects of monochromatic blue light (B, 460 nm), monochromatic red light (R, 660 nm), white light (CK), and different combinations of R and B (R9B1, R7B3, R5B5, R3B7, and R1B9) on the growth of, and flavonoid accumulation in S. baicalensis. The results revealed that under R:B ratios of 9:1 or 7:3, the whole plant and roots of S. baicalensis seedlings had a higher biomass and flavonoid content. Targeted metabolomics analysis showed that 48 differentially expressed metabolites (DEMs) were verified between different groups, and the number of upregulated DEMs, particularly flavonoids, were higher in the R9B1 and R7B3 groups compared with the CK. Transcriptome data identified 1412 and 1508 differentially expressed genes (DEGs) in the R9B1 and R7B3 groups, respectively, in contrast to the CK. KEGG pathway analysis indicated that DEGs in both the R9B1 and R7B3 groups were primarily enriched in the phenylpropane biosynthesis, plant hormone signal transduction, flavonoid biosynthesis, starch and sucrose metabolism, galactose metabolism, cartenoid biosynthesis, zeatin biosynthesis, and nitrogen metabolism pathways. The qRT-PCR results showed that SbPAL, SbCLL-7, SbCHI, SbFNS, and SbOMT encoding enzymes for the flavonoid biosynthesis pathway were significantly upregulated in S. baicalensis, which was consistent with the transcriptome data. Finally, based on correlation analysis between the main flavonoids in S. baicalensis and the genes encoding transcription factors and enzymes for the flavonoid metabolism pathway, a co-expression network map was developed, which provided a basis for the mining of light responsive genes related to flavonoids biosynthesis in S. baicalensis. This is the first report to articulate how the combination of red light and blue light influences the growth and secondary metabolism of S. baicalensis.